Graph data, essential in fields like knowledge representation and social networks, often involves large networks with many nodes and edges. Transmitting these graphs can be highly inefficient due to their size and redundancy for specific tasks. This paper introduces a method to extract a smaller, task-focused subgraph that maintains key information while reducing communication overhead. Our approach utilizes graph neural networks (GNNs) and the graph information bottleneck (GIB) principle to create a compact, informative, and robust graph representation suitable for transmission. The challenge lies in the irregular structure of graph data, making GIB optimization complex. We address this by deriving a tractable variational upper bound for the objective function. Additionally, we propose the VQ-GIB mechanism, integrating vector quantization (VQ) to convert subgraph representations into a discrete codebook sequence, compatible with existing digital communication systems. Our experiments show that this GIB-based method significantly lowers communication costs while preserving essential task-related information. The approach demonstrates robust performance across various communication channels, suitable for both continuous and discrete systems.

翻译：图数据在知识表示和社交网络等领域至关重要，通常涉及包含大量节点和边的大型网络。由于这些图的规模庞大且对于特定任务存在冗余，直接传输它们可能效率极低。本文提出一种方法，旨在提取一个更小、面向任务的子图，该子图能在保持关键信息的同时降低通信开销。我们的方法利用图神经网络（GNNs）和图信息瓶颈（GIB）原理，创建一个紧凑、信息丰富且鲁棒的图表示，适合传输。挑战在于图数据的不规则结构使得GIB优化变得复杂。我们通过推导目标函数的一个可处理的变分上界来解决这一问题。此外，我们提出了VQ-GIB机制，集成向量量化（VQ）将子图表示转换为离散码本序列，从而与现有的数字通信系统兼容。实验表明，这种基于GIB的方法在保持基本任务相关信息的同时，显著降低了通信成本。该方法在各种通信信道中均表现出鲁棒的性能，适用于连续和离散系统。